{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import tqdm\n",
    "current_directory = os.getcwd()\n",
    "upper_directory = os.path.abspath(os.path.join(current_directory, \"..\"))\n",
    "sys.path.append(upper_directory)\n",
    "\n",
    "\n",
    "## ================================================================= ##\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import yaml\n",
    "\n",
    "from model.Lorenz_model import L96_foward_model, L96_inv_obs_model\n",
    "from utils import dict2namespace\n",
    "from var_opt.Tensor_Var import Tensor_Var\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "random_seed = 2025\n",
    "np.random.seed(random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using cuda:1 device\n",
      "[INFO]  _CudaDeviceProperties(name='NVIDIA GeForce RTX 4090', major=8, minor=9, total_memory=24195MB, multi_processor_count=128)\n"
     ]
    }
   ],
   "source": [
    "working_dir = \"../training_scripts/L96_D40\"\n",
    "\n",
    "device = \"cuda:1\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"[INFO] Using {} device\".format(device))\n",
    "print(\"[INFO] \", torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU')\n",
    "\n",
    "config_path = os.path.join(working_dir, 'config.yaml')\n",
    "with open(config_path, 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "config = dict2namespace(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the parameters\n",
    "n_state = 40\n",
    "n_obs = int(n_state * 0.2)\n",
    "\n",
    "ass_w = 5 # assimilation window \n",
    "ass_T = ass_w * 20\n",
    "\n",
    "num_mc = 10 # number of repeated experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Positional Encoding is used\n",
      "[INFO] Input size of the fully connected layer:  384\n",
      "[INFO] Freezing phi_S\n",
      "[INFO] Freezing phi_inv_S\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2144838/1095155476.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  forward_model.load_state_dict(torch.load(working_dir + '/model_weights/forward_model.pt', map_location=\"cpu\"))\n",
      "/tmp/ipykernel_2144838/1095155476.py:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  forward_model.C_fwd = torch.load(working_dir + '/model_weights/C_fwd.pt', map_location=\"cpu\")\n",
      "/tmp/ipykernel_2144838/1095155476.py:7: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  inv_obs_model.load_state_dict(torch.load(working_dir + '/model_weights/inv_obs_model.pt', map_location=\"cpu\"))\n",
      "/tmp/ipykernel_2144838/1095155476.py:11: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  Q = torch.load(working_dir + '/' + 'Q.pt', map_location=\"cpu\")\n"
     ]
    }
   ],
   "source": [
    "# Load models\n",
    "forward_model = L96_foward_model(config)\n",
    "forward_model.load_state_dict(torch.load(working_dir + '/model_weights/forward_model.pt', map_location=\"cpu\"))\n",
    "forward_model.C_fwd = torch.load(working_dir + '/model_weights/C_fwd.pt', map_location=\"cpu\")\n",
    "\n",
    "inv_obs_model = L96_inv_obs_model(config)\n",
    "inv_obs_model.load_state_dict(torch.load(working_dir + '/model_weights/inv_obs_model.pt', map_location=\"cpu\"))\n",
    "\n",
    "# Load error covariance matrices\n",
    "try:\n",
    "    Q = torch.load(working_dir + '/' + 'Q.pt', map_location=\"cpu\")\n",
    "    R = torch.load(working_dir + '/' + 'R.pt', map_location=\"cpu\")\n",
    "    B = torch.load(working_dir + '/' + 'B.pt', map_location=\"cpu\")\n",
    "except:\n",
    "    hidden_dim = forward_model.hidden_dim\n",
    "    B = np.eye(hidden_dim) * 0.1\n",
    "    R = np.eye(hidden_dim) \n",
    "    Q = np.eye(hidden_dim) * 0.1\n",
    "\n",
    "DA_Tensor_Var = Tensor_Var(forward_model=forward_model, \n",
    "                            observation_model=inv_obs_model, \n",
    "                            n_state=n_state, \n",
    "                            n_obs=n_obs,\n",
    "                            B=B,\n",
    "                            Q=Q,\n",
    "                            R=R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2144838/3413160971.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  z_b = torch.load(working_dir + '/model_weights/z_b.pt', map_location=\"cpu\")\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "data_save_path = '../data/L96_data_dim40'\n",
    "test_seq_state = np.load(data_save_path + '/test_seq_state.npy', mmap_mode='r')[:, 500:, ...]\n",
    "test_seq_obs = np.load(data_save_path + '/test_seq_obs.npy', mmap_mode='r')[:, 500:, ...]\n",
    "test_seq_hist = np.load(data_save_path + '/test_seq_hist.npy', mmap_mode='r')[:, 500:, ...]\n",
    "\n",
    "max_value = test_seq_state.max()\n",
    "min_value = test_seq_state.min()\n",
    "\n",
    "z_b = torch.load(working_dir + '/model_weights/z_b.pt', map_location=\"cpu\")\n",
    "\n",
    "test_seq_state = test_seq_state[:num_mc]\n",
    "test_seq_obs = test_seq_obs[:num_mc]\n",
    "test_seq_hist = test_seq_hist[:num_mc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Assimilation Window Length: 5\n",
      "[INFO] Traj Length: 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DA Evaulation:   0%|          | 0/10 [00:00<?, ?it/s]/home/yiming/Documents/Yiming/Tensor-Var/var_opt/Tensor_Var.py:77: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n",
      "  hist = torch.Tensor(seq_hist[start:end]).permute(0, 2, 1)\n",
      "100%|██████████| 20/20 [00:00<00:00, 35.04it/s]\n",
      "DA Evaulation:  10%|█         | 1/10 [00:00<00:05,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 0th traj Estimation Error: 0.09268279413118195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.22it/s]\n",
      "DA Evaulation:  20%|██        | 2/10 [00:01<00:04,  1.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 1th traj Estimation Error: 0.09476412455123343\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.65it/s]\n",
      "DA Evaulation:  30%|███       | 3/10 [00:01<00:03,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2th traj Estimation Error: 0.09125881009952193\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.92it/s]\n",
      "DA Evaulation:  40%|████      | 4/10 [00:02<00:03,  1.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 3th traj Estimation Error: 0.08864503642044158\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 34.87it/s]\n",
      "DA Evaulation:  50%|█████     | 5/10 [00:02<00:02,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 4th traj Estimation Error: 0.08864120419689839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.03it/s]\n",
      "DA Evaulation:  60%|██████    | 6/10 [00:03<00:02,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 5th traj Estimation Error: 0.08706238956710322\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.40it/s]\n",
      "DA Evaulation:  70%|███████   | 7/10 [00:03<00:01,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 6th traj Estimation Error: 0.08515780897293454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.17it/s]\n",
      "DA Evaulation:  80%|████████  | 8/10 [00:04<00:01,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 7th traj Estimation Error: 0.09688790491731225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 35.60it/s]\n",
      "DA Evaulation:  90%|█████████ | 9/10 [00:05<00:00,  1.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 8th traj Estimation Error: 0.09409631344189422\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:00<00:00, 36.05it/s]\n",
      "DA Evaulation: 100%|██████████| 10/10 [00:05<00:00,  1.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 9th traj Estimation Error: 0.08170476243079564\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_list = np.zeros(num_mc)\n",
    "time_list = np.zeros(num_mc)\n",
    "print(\"[INFO] Assimilation Window Length: {}\".format(ass_w))\n",
    "print(\"[INFO] Traj Length: {}\".format(ass_T))\n",
    "for i in tqdm.tqdm(range(num_mc), desc=\"DA Evaulation\"):\n",
    "    traj_true = test_seq_state[i, :ass_T]\n",
    "    traj_estimation, eval_time = DA_Tensor_Var.perform_4DVar(test_seq_obs[i], \n",
    "                                                             seq_hist=test_seq_hist[i], \n",
    "                                                             z_b=z_b, \n",
    "                                                             assimilation_window=ass_w, \n",
    "                                                             T=ass_T)\n",
    "    rmse = np.sqrt( np.mean( (np.array(traj_estimation) - traj_true)**2) )/(max_value-min_value)\n",
    "    error_list[i] = rmse\n",
    "    time_list[i] = eval_time\n",
    "    print(\"[INFO] {}th traj Estimation Error: {}\".format(i,rmse))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(24, 10))\n",
    "ax[0].imshow(np.array(traj_estimation).T, aspect=\"auto\")\n",
    "ax[0].set_title('Estimated Trajectory')\n",
    "ax[1].imshow(traj_true.T, aspect=\"auto\")\n",
    "ax[1].set_title('True Trajectory')\n",
    "cbar = ax[2].imshow(np.abs(np.array(traj_estimation).T - traj_true.T), aspect=\"auto\")\n",
    "plt.colorbar(cbar, ax=ax[2])\n",
    "ax[2].set_title('Error')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PODA",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
